From Man vs. Machine to Man + Machine

Bhaswar Chakma

2025-05-20

A Moment in History: Man vs. Machine

  • In 1997, world champion Garry Kasparov lost to IBM’s Deep Blue.

From Adversaries to Allies

  • After losing, Kasparov didn’t give up — he adapted.

  • He introduced “centaur chess”, pairing human intuition with machine power.

From Chess to Finance

Kasparov’s idea of man + machine wasn’t just for chess.

This study brings that question into finance:

  • Can human analysts and AI models compete — or collaborate — in forecasting the market?

  • To explore this, the authors created an AI analyst and compared it to real professionals.

Meet the Analysts

Human Analyst
  • Uses intuition and experience
  • Reads disclosures, meets firms
  • Prone to cognitive or incentive bias

AI Analyst
  • Absorbs massive data
  • Fast, consistent, unbiased
  • But lacks nuance or context

Research Questions

This study addresses three core questions:

  1. Can an AI analyst predict 12-month-ahead stock returns more accurately than human analysts?

  2. When do human analysts outperform AI — and why?

  3. Does combining AI and human forecasts improve accuracy and reduce large errors?

The Prediction Setup

In this study, both human and AI analysts forecast 12-month-ahead stock returns using target prices.

  • Target prices reflect analysts’ valuation beliefs

  • Predicted return = (Target – Current Price) / Current Price

  • Target prices are preferred over earnings forecasts:

    • Reflect longer-term firm value
    • Less affected by short-term managerial manipulation

Methods: Overview

The AI analyst is an ensemble of three models — combining strengths across ML and deep learning.

  • Random Forest

  • Gradient Boosting

  • LSTM (Long Short-Term Memory)

Final forecast = median prediction across all three models

Methodology: Training Setup

  • Forecasts are made using a rolling 3-year window of past data
    → Simulates real-time prediction, avoiding lookahead bias

  • The AI model uses only public information
    → No human analyst inputs, no insider data

The Data: Sample Overview

  • Data sources:

    • I/B/E/S, Compustat, CRSP (structured data)
    • RavenPack (news sentiment), Twitter (social media)
    • SEC EDGAR (filings), Kogan et al. (2017) (patents)
  • Over 1.15 million target price forecasts, covering thousands of U.S. firms

  • Coverage spans all major sectors, firm sizes, and time periods (2001–2018)

The Data: AI Analyst Input Overview

The AI analyst is trained on a wide variety of publicly available information.

  • Human analyst data: I/B/E/S target prices (2001–2018)
  • AI model: trained on 3 years of historical data prior to each forecast
  • Forecast target: 12-month stock return based on target price

Data Input Categories

The model draws from six key categories of inputs:

The Data: Structured Inputs

Firm Data: - Size, book-to-market, ROA - Leverage, accruals - Past returns (1–36 months), volatility - Amihud illiquidity

Industry Data: - Fama-French industry groupings - Industry competition (text-based) - Product market fluidity

Macro Data: - GDP growth, CPI, oil prices - Treasury yields, credit spreads - Market-level illiquidity

The Data: Textual and Alternative Inputs

Filings (10-K, 10-Q, 8-K): - Sentiment and tone (Loughran–McDonald) - Readability and complexity - Text similarity and novelty

Sentiment: - RavenPack news sentiment - Twitter-based firm sentiment (Cao et al. 2021a)

Innovation: - Patent value estimates (Kogan et al. 2017)

Results: Performance – AI vs. Human

AI uses only public data, with no access to analyst forecasts or private information.

  • The AI analyst outperforms human analysts in approximately 54.5% of stock forecasts.
    • This outperformance is consistent across time, sectors, and market conditions.

Results: What Drives the AI Analyst?

Results: When Humans Outperform AI

While the AI model performs better overall, human analysts outperform in specific contexts:

  • Firms with high intangible assets (e.g. R&D, brand value)
  • Firms facing financial distress or complex restructuring
  • Stocks with low liquidity or limited trading activity
  • Situations requiring qualitative judgment or soft information

Results: Man + Machine – The Hybrid Model

Combining AI forecasts with analyst predictions and profiles creates a hybrid “centaur” analyst.

  • The hybrid model slightly outperforms standalone AI in forecast accuracy.
  • It improves consistency and reduces large forecast errors.
  • Man + Machine captures the strengths of both:
    • AI’s scale and objectivity
    • Human analysts’ intuition and contextual judgment

The Hybrid Model Avoids Extreme Errors

  • Extreme errors = forecasts in the top 10% of squared prediction errors

  • Man + Machine avoids:

    • 90.7% of extreme errors made by human analysts
    • 43.6% of extreme errors made by the AI model

Conclusion

  1. Can AI outperform human analysts?
    • Yes, AI beats the best human analyst in ~54.5% of cases using only public data.
  2. When do human analysts outperform AI?
    • In complex settings like illiquidity, distress, and high intangibles — where qualitative judgment matters.
  3. Can combining AI and human forecasts improve results?
    • Yes, the hybrid model slightly improves accuracy and greatly reduces extreme forecasting errors.

Key Opportunities for Improvement

  1. Model Interpretability: Use SHAP values (SHapley Additive exPlanations) or partial dependence plots to explain predictions.

  2. Robustness Across Market Conditions: Assess performance across macroeconomic regimes (e.g., recessions vs. expansions) to test model stability under systemic market stress.